NURBS曲面的算法分析及实现

讨论了ＮＵＲＢＳ曲线和节点矢量、控制点和基函数对曲面形状的影响，给出了ＮＵＲＢＳ的算法分析及其实现方案，最后，将ＮＵＲＢＳ曲面与老式曲面（如Ｂ样条、Ｂｅｚｉｅｒ曲面等）进行了比较。     

切入“历史”后的视点——论“新历史小说”的叙事角度

“新历史小说”，自生成之日起就存在着命名、指认等一系列问题上的争议，这一现象的出现，除了与其自身内涵和外延的不确定性有关之外，还与其叙事上的独特角度密不可分。有鉴于此，结合“新历史小说”出现的时代背景，通过对其结构特质的三个主要方面，即第一人称视角与“历史”同步、聚焦变换与情感演绎、民间视野及其视点应用进行阐释，就成为一道重要的逻辑线索。     

Bayesian validation assessment of multivariate computational models

Multivariate model validation is a complex decision-making problem involving comparison of multiple correlated quantities, based upon the available information and prior knowledge. This paper presents a Bayesian risk-based decision method for validation assessment of multivariate predictive models under uncertainty. A generalized likelihood ratio is derived as a quantitative validation metric based on Bayes’ theorem and Gaussian distribution assumption of errors between validation data and model prediction. The multivariate model is then assessed based on the comparison of the likelihood ratio with a Bayesian decision threshold, a function of the decision costs and prior of each hypothesis. The probability density function of the likelihood ratio is constructed using the statistics of multiple response quantities and Monte Carlo simulation. The proposed methodology is implemented in the validation of a transient heat conduction model, using a multivariate data set from experiments. The Bayesian methodology provides a quantitative approach to facilitate rational decisions in multivariate model assessment under uncertainty.     

私营企业与经济发展水平相关性分析

私营企业与一个国家或地区的经济社会发展有着密切的关系。本文通过实证研究,分析了私营企业发展与经济发展的相关关系,认为私营企业的发展是经济发展的重要增长点。最后结合分析结果提出了发展私营企业进而促进经济可持续增长的一些建议。     

运用心理学 讲好网络课

研究如何将心理学认知学习论运用在教学中. 强调新知识必须在学生的认知结构中找到固定点. 在分析 <计算机网络> 课程知识点的逻辑结构的基础上, 介绍了将新知识植入学生认知结构的讲授方法. 并探讨了几种固定新知识的链条和扫除学生认知结构断裂点的途径.     

BATHTUB-TYPE CURVES IN RELIABILITY AND BEYOND

Finding optimal, or at least good, maintenance and repair policies is crucial in reliability engineering. Likewise, describing life phases of human mortality is important when determining social policy or insurance premiums. In these tasks, one searches for distributions to fit data and then makes inferences about the population(s). In the present paper, we focus on bathtub‐type distributions and provide a view of certain problems, methods and solutions, and a few challenges, that can be encountered in reliability engineering, survival analysis, demography and actuarial science.     

谈分级显性意义假说对反语中的认知参照点的解释力

本文运用Giora的分级显性意义假说分析了Langacker的认知参照点理论，并对反语中的认知参照点进行分析，得出反语中的认知参照点也是层级突显的结论，为反语的理解提供一个新的视角。     

Consistent maximum likelihood estimation of a unimodal density using shape restrictions

It is well known that the unimodal maximum likelihood estimator of a density is consistent everywhere but at the mode. The authors review various ways to solve this problem and propose a new estimator that is concave over an interval containing the mode; this interval may be chosen by the user or through an algorithm. The authors show how to implement their solution and compare it to other approaches through simulations. They show that the new estimator is consistent everywhere and determine its rate of convergence in the Hellinger metric.     

An affine‐invariant multivariate sign test for cluster correlated data

The author presents a multivariate location model for cluster correlated observations. He proposes an affine‐invariant multivariate sign statistic for testing the value of the location parameter. His statistic is an adaptation of that proposed by Randles (2000). The author shows, under very mild conditions, that his test statistic is asymptotically distributed as a chi‐squared random variable under the null hypothesis. In particular, the test can be used for skewed populations. In the context of a general multivariate normal model, the author obtains values of his test's Pitman asymptotic efficiency relative to another test based on the overall average. He shows that there is an improvement in the relative performance of the new test as soon as intra‐cluster correlation is present Even in the univariate case, the new test can be very competitive for Gaussian data. Furthermore, the statistic is easy to compute, even for large dimensional data. The author shows through simulations that his test performs well compared to the average‐based test. He illustrates its use with real data.     

Causal Reasoning from Longitudinal Data*

Abstract. This paper reviews some of the key statistical ideas that are encountered when trying to find empirical support to causal interpretations and conclusions, by applying statistical methods on experimental or observational longitudinal data. In such data, typically a collection of individuals are followed over time, then each one has registered a sequence of covariate measurements along with values of control variables that in the analysis are to be interpreted as causes, and finally the individual outcomes or responses are reported. Particular attention is given to the potentially important problem of confounding. We provide conditions under which, at least in principle, unconfounded estimation of the causal effects can be accomplished. Our approach for dealing with causal problems is entirely probabilistic, and we apply Bayesian ideas and techniques to deal with the corresponding statistical inference. In particular, we use the general framework of marked point processes for setting up the probability models, and consider posterior predictive distributions as providing the natural summary measures for assessing the causal effects. We also draw connections to relevant recent work in this area, notably to Judea Pearl's formulations based on graphical models and his calculus of so‐called do‐probabilities. Two examples illustrating different aspects of causal reasoning are discussed in detail.